Type

Journal Article

Authors

Lorraine Brennan
Isobel Claire Gormley
Gift Nyamundanda

Subjects

Mathematics

Topics
data model analysis principal components analysis probabilistic principal components analysis linear mixed model metabolomics auto regressive model linear mixed models longitudinal metabolomic data

A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data (2014)

Abstract In a longitudinal metabolomics study, multiple metabolites are measured from several observations at many time points. Interest lies in reducing the dimensionality of such data and in highlighting influential metabolites which change over time. A dynamic probabilistic principal components analysis model is proposed to achieve dimension reduction while appropriately modelling the correlation due to repeated measurements. This is achieved by assuming an auto-regressive model for some of the model parameters. Linear mixed models are subsequently used to identify influential metabolites which change over time. The model proposed is used to analyse data from a longitudinal metabolomics animal study.
Collections Ireland -> University College Dublin -> Conway Institute Research Collection
Ireland -> University College Dublin -> Conway Institute
Ireland -> University College Dublin -> Mathematics and Statistics Research Collection
Ireland -> University College Dublin -> Agriculture and Food Science Research Collection

Full list of authors on original publication

Lorraine Brennan, Isobel Claire Gormley, Gift Nyamundanda

Experts in our system

1
Lorraine Brennan
University College Dublin
Total Publications: 166
 
2
Isobel Claire Gormley
University College Dublin
Total Publications: 25